Available masters and doctoral projects

An overview of opportunities for students to work with scientists at Reykjavik University on research projects. Please contact the person listed within each project for further information.

Undergraduate level projects are on the Icelandic portion of RU's website: Available undergraduate level projects

Smart Microcontroller-Based Integrated Monitoring and Protection System for Three-Phase Power Transformers

MSc level project, School of Science and Engineering

  • msc_mc-transformer-protection-fig

Three-phase power transformers are important equipment in AC electrical power systems. In this project, a smart microcontroller-based integrated monitoring and protection system for three-phase power transformers will be developed theoretically and an experimental prototype model (software and hardware) will be designed, implemented and tested.

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Smart Reconfiguration of Electric Power Distribution Networks for Power Loss Minimization and Voltage Profile Optimization

MSc level project, School of Science and Engineering

  • msc_smart-reconfiguration-fig

The reconfiguration process changes the network topology by changing the open/close status of the switches to improve the performance without customers' interruptions. This project proposes an intelligent-based autonomous smart reconfiguration technique for power loss minimization and voltage profile optimization.

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Quality of Sleep

PhD level project, School of Computer Science

  • Quality of Sleep

Sleep quality is normally determined by analyzing data collected by electroencephalogram (EEG), an electrooculogram (EOG), and an electromyogram (EMG) signals. The sleep quality is defined as having an uninterrupted sleep profile, having sufficient amount of deep sleep, and having few arousals. During sleep several physiological processes change depending on sleep stage and arousals. These physiological processes include respiratory and cardiac processes which can be measured in a relatively simple sleep study. The project aims to develop an algorithm to measure sleep quality using signals from sleep recordings, other than the EEG, EOG, and EMG signals.

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Snore detection

MSc level project, School of Science and Engineering

  • Snore detection

Develop an algorithm to automatically classify snores. Snores can originate from different sites in the upper airway and snores originating from different places may have specific properties such as relative power distribution in certain bands. During sleep a range of breathing noises can be detected such as mouth breathing, loud breathing, and snoring. Snoring can occur as an individual event or a series of events, snore trains. Snoring is not used in routine diagnosis of sleep disorders, snore characteristics have been linked to specific sites of snore origin. The method will be developed by analyzing a data set containing manually scored snore events of subjects sleeping in a controlled environment, and of subjects sleeping in their homes.

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Arousal detection in EEG

MSc level project, School of Science and Engineering

  • Arousal Detection in EEG

Develop an algorithm to automatically score cortical arousal in EEG signals measured during sleep. According to the scoring rules set forward by the American Academy of Sleep Medicine, arousals are short events, lasting at least 3 seconds occurring on average 10-20 times per hour of sleep in healthy people, but may occur hundreds of times per hour in people suffering sleep disorders. EEG arousals are identified by abrupt shifts in EEG frequency. The method will be developed using a large data set of manually scored EEG data.

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Finite Element simulations of a side-ways fall to the hip: Identification of critical material parameters

MSc level project, School of Science and Engineering

  • Finite Element simulations of a side-ways fall to the hip: Identification of critical material parameters

X-ray Computed Tomography (CT) based Finite element models have the potential to improve the prediction of hip fracture risk for osteoporotic patients compared to current standard of diagnosis. The aim of this project is to use such models for investigating the influence of material parameters of bone, cartilage, ligaments and soft tissue on the fracture risk prediction.

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Feature extraction for deep neural networks speech recognition

PhD level project, School of Science and Engineering

  • Spectrograms of syllables dee dah doo

The aim of this project is to extract features from the speech signal to use for speech recognition. These features might be engineered by using for example designed deterministic transforms such as wavelets or an adaptive signal processing method such as matching pursuit or a combination of both. The method will be evaluated on a large continuous speech recognition system developed by the Language and Voice Lab.

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Finite Element simulations of a side-ways fall to the hip: Investigation of critical and non-critical alignment

MSc level project, School of Science and Engineering

  • Finite Element simulations of a side-ways

X-ray Computed Tomography (CT) based Finite element models have the potential to improve the prediction of hip fracture risk for osteoporotic patients compared to current standard of diagnosis. The aim of this project is to use such models for investigating the influence of leg and pelvic alignment on the fracture outcome.

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